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A weighted bootstrap approach to bootstrap iteration

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  • P. Hall
  • Y. Maesono

Abstract

The operation of resampling from a bootstrap resample, encountered in applications of the double bootstrap, maybe viewed as resampling directly from the sample but using probability weights that are proportional to the numbers of times that sample values appear in the resample. This suggests an approximate approach to double‐bootstrap Monte Carlo simulation, where weighted bootstrap methods are used to circumvent much of the labour involved in compounded Monte Carlo approximation. In the case of distribution estimation or, equivalently, confidence interval calibration, the new method may be used to reduce the computational labour. Moreover, the method produces the same order of magnitude of coverage error for confidence intervals, or level error for hypothesis tests, as a full application of the double bootstrap.

Suggested Citation

  • P. Hall & Y. Maesono, 2000. "A weighted bootstrap approach to bootstrap iteration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 137-144.
    • Handle: RePEc:bla:jorssb:v:62:y:2000:i:1:p:137-144
    DOI: 10.1111/1467-9868.00224
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    Cited by:

    1. Miguel Martínez Comesaña & Sandra Martínez Mariño & Pablo Eguía Oller & Enrique Granada Álvarez & Aitor Erkoreka González, 2020. "A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
    2. Timothy Neal, 2023. "The Importance of External Weather Effects in Projecting the Economic Impacts of Climate Change," Discussion Papers 2023-09, School of Economics, The University of New South Wales.
    3. Shimodaira, Hidetoshi, 2014. "Higher-order accuracy of multiscale-double bootstrap for testing regions," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 208-223.

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